---
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Improved-Arabert-twitter-sentiment2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Improved-Arabert-twitter-sentiment2

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4308
- Accuracy: 0.8759

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.07  | 50   | 0.4102          | 0.8130   |
| No log        | 0.14  | 100  | 0.3141          | 0.8769   |
| No log        | 0.21  | 150  | 0.2981          | 0.8806   |
| No log        | 0.27  | 200  | 0.3297          | 0.8769   |
| No log        | 0.34  | 250  | 0.2998          | 0.8796   |
| No log        | 0.41  | 300  | 0.3312          | 0.8630   |
| No log        | 0.48  | 350  | 0.3615          | 0.8491   |
| No log        | 0.55  | 400  | 0.3695          | 0.8481   |
| No log        | 0.62  | 450  | 0.3094          | 0.8778   |
| 0.316         | 0.68  | 500  | 0.2784          | 0.8907   |
| 0.316         | 0.75  | 550  | 0.3404          | 0.8759   |
| 0.316         | 0.82  | 600  | 0.3045          | 0.8806   |
| 0.316         | 0.89  | 650  | 0.3435          | 0.8731   |
| 0.316         | 0.96  | 700  | 0.2849          | 0.9      |
| 0.316         | 1.03  | 750  | 0.2846          | 0.8963   |
| 0.316         | 1.1   | 800  | 0.3034          | 0.8926   |
| 0.316         | 1.16  | 850  | 0.3801          | 0.8787   |
| 0.316         | 1.23  | 900  | 0.3525          | 0.8898   |
| 0.316         | 1.3   | 950  | 0.3388          | 0.8889   |
| 0.2119        | 1.37  | 1000 | 0.3823          | 0.8843   |
| 0.2119        | 1.44  | 1050 | 0.3621          | 0.8935   |
| 0.2119        | 1.51  | 1100 | 0.4106          | 0.8843   |
| 0.2119        | 1.58  | 1150 | 0.3820          | 0.8870   |
| 0.2119        | 1.64  | 1200 | 0.3770          | 0.8796   |
| 0.2119        | 1.71  | 1250 | 0.4199          | 0.8824   |
| 0.2119        | 1.78  | 1300 | 0.4308          | 0.8759   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1